Systems and methods for prioritizing driver warnings in a vehicle

ABSTRACT

The disclosure is generally directed to systems and methods for prioritizing driver warnings in a vehicle. In one example, a first sensor in the vehicle may detect a first driving event, such as, for example, another vehicle that is traveling in a blind spot of the driver. A second sensor in the vehicle may detect a second driving event, such as, for example, that the driver of the vehicle is tailgating another vehicle. A driver warning system in the vehicle produces a driver warning based on prioritizing one of the two driving events. For example, the driver warning system may prioritize a warning pertaining to the tailgating action over a warning pertaining to the vehicle moving in the blind spot. In some cases, the prioritization may be carried out by using historical information about the two driving events.

BACKGROUND

Modern vehicles typically include sensors/detectors that issue varioustypes of warnings to a driver. In some cases, the warnings may be issuedin the form of audible signals. In other cases, the warnings may beissued in the form of images or text upon a display screen. In yet otherinstances, the warnings may be issued in the form of a hapticsignal—such as a vibration in the steering wheel, for example.

It is desirable to have systems and methods for prioritizing the driverwarnings in a vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

A detailed description is set forth below with reference to theaccompanying drawings. The use of the same reference numerals mayindicate similar or identical items. Various embodiments may utilizeelements and/or components other than those illustrated in the drawings,and some elements and/or components may not be present in variousembodiments. Elements and/or components in the figures are notnecessarily drawn to scale. Throughout this disclosure, depending on thecontext, singular and plural terminology may be used interchangeably.

FIG. 1 illustrates an example driver warning system provided in avehicle in accordance with an embodiment of the disclosure.

FIG. 2 illustrates a first example driving event that may be addressableby a driver warning system in accordance with the disclosure.

FIG. 3 illustrates a second driving event that may be addressable by adriver warning system in accordance with the disclosure.

FIG. 4 illustrates a third example driving event that may be addressableby a driver warning system in accordance with the disclosure.

FIG. 5 shows some example components that can be included in a driverwarning system in accordance with an embodiment of the disclosure.

FIG. 6 illustrates a configuration that allows a driver warning systemto communicate with external components for sharing informationpertaining to driver warnings in accordance with an embodiment of thedisclosure.

DETAILED DESCRIPTION Overview

In terms of a general overview, embodiments described in this disclosureare directed to systems and methods for prioritizing driver warnings ina vehicle. In one driving scenario example, a first sensor in thevehicle may detect a first event, such as, for example, another vehiclethat is traveling in a blind spot of the driver. A second sensor in thevehicle may detect a second event, such as, for example, that the driverof the vehicle is tailgating another vehicle. A driver warning system inthe vehicle may produce a driver warning based on prioritizing one ofthe two events within the driving scenario. In the example describedabove, the driver warning system may prioritize a warning pertaining tothe tailgating action over a warning pertaining to the other vehiclemoving in the blind spot. In some instances, the prioritization may becarried out by using historical information about the two events withinthe driving scenario. The historical information may be obtained viacrowdsourcing in some implementations.

Illustrative Embodiments

The disclosure will be described more fully hereinafter with referenceto the accompanying drawings, in which example embodiments of thedisclosure are shown. This disclosure may, however, be embodied in manydifferent forms and should not be construed as limited to the exampleembodiments set forth herein. It will be apparent to persons skilled inthe relevant art that various changes in form and detail can be made tovarious embodiments without departing from the spirit and scope of thepresent disclosure. Thus, the breadth and scope of the presentdisclosure should not be limited by any of the above-described exampleembodiments but should be defined only in accordance with the followingclaims and their equivalents. The description below has been presentedfor the purposes of illustration and is not intended to be exhaustive orto be limited to the precise form disclosed. It should be understoodthat alternate implementations may be used in any combination desired toform additional hybrid implementations of the present disclosure. Forexample, any of the functionality described with respect to a particulardevice or component may be performed by another device or component.Furthermore, while specific device characteristics have been described,embodiments of the disclosure may relate to numerous other devicecharacteristics. Further, although embodiments have been described inlanguage specific to structural features and/or methodological acts, itis to be understood that the disclosure is not necessarily limited tothe specific features or acts described. Rather, the specific featuresand acts are disclosed as illustrative forms of implementing theembodiments.

Certain words and phrases are used herein solely for convenience andsuch words and terms should be interpreted as referring to variousobjects and actions that are generally understood in various forms andequivalencies by persons of ordinary skill in the art. For example, theword “sensor” may be used interchangeably with the word “detector.”Either word as used in this disclosure refers to various devices, suchas an ultrasonic sensor that may be used to detect an object by usingultrasonic waves, a radar detector that may be used to detect an objectby using radar signals, and an imaging device, such as a camera that isused to capture an image (or a video clip) of an object for imageprocessing by an image processing element to detect the object. The word“data” may be used interchangeably with the word “information.” Eitherword pertains to any of various forms of input to a processor. The word“condition” may be used interchangeably with words, such as “situation”and “scenario.” It should be understood that the word “example” as usedherein is intended to be non-exclusionary and non-limiting in nature.

FIG. 1 illustrates an example driver warning system 120 provided in avehicle 105 in accordance with an embodiment of the disclosure. Thevehicle 105 may be any of various types of vehicles that include varioussensors and detectors for detecting various types of events that may beencountered by a driver 150 when operating the vehicle 105. A fewexample sensors that may be provided in the vehicle 105 may include arear-facing camera 155 mounted upon a trunk portion of the vehicle 105,a radar sensor 160 mounted upon a rear bumper of the vehicle 105, afront-facing camera 115 mounted upon a hood portion of the vehicle 105,a radar sensor 110 mounted upon a front bumper of the vehicle 105, anultrasonic sensor 125 mounted upon a driver-side rear-view mirror, anultrasonic sensor 145 mounted upon a passenger-side rear-view mirror,and a camera 130 mounted upon the front windshield of the vehicle 105and facing the driver 150 of the vehicle 105.

The vehicle 105 may further include components, such as, for example, avehicle computer 170, an infotainment system 140, and a beeper 135.These components may be communicatively coupled to the driver warningsystem 120. The vehicle computer 170 may perform various functions ofthe vehicle 105, such as controlling engine operations (fuel injection,speed control, emissions control, braking, etc.), managing climatecontrols (air conditioning, heating etc.), activating airbags, andissuing warnings (check engine light, bulb failure, low tire pressure,etc.). The vehicle computer 170 may also provide various type ofinformation to the driver warning system 120, such as certain actionsperformed by the driver 150. For example, the vehicle computer 170 maymonitor the fuel injection system and/or a braking system of the vehicle105 to determine that the driver 150 is executing a braking operationupon the vehicle 105. The braking operation can be intimated to thedriver warning system 120 in response to a request from the driverwarning system 120.

The infotainment system 140 may include a combination of variousentertainment devices (such as a radio, streaming audio solutions, andUSB access ports for digital audio devices) with elements, such as anavigation system that provides navigation instructions and maps upon adisplay screen of the infotainment system 140.

The various sensors are communicatively coupled to the driver warningsystem 120 and configured to provide signals that may be processed by aprocessor 121 for evaluating various events within a driving scenario.For example, the radar sensor 160 and/or the radar sensor 110 mayprovide signals to the driver warning system 120 upon detecting anothervehicle traveling closely to the vehicle 105. The processor 121 mayevaluate these signals and decide to warn the driver 150 by transmittingaudio beeps through the beeper 135, and/or a warning message that may beflashed upon a display screen of the infotainment system 140 advisingthe driver 150 to switch lanes, for example.

As another example, the front-facing camera 115 and/or the rear-facingcamera 155 may provide images to the driver warning system 120 that areexamined by the processor 121 to detect any event that warrantsproviding a warning to the driver 150. In one implementation, thefront-facing camera 115 and/or the rear-facing camera 155 may providethe images to the driver warning system 120 in the form of a videostream. The processor 121 may evaluate the video stream in real time toidentify any event that warrants issuing a warning to the driver 150.

As yet another example, the ultrasonic sensor 125 mounted upon thedriver-side rear-view mirror may detect another vehicle located in adriver-side blind spot and provide signals to the driver warning system120. The processor 121 may evaluate these signals and decide to warn thedriver 150 by providing a flashing light sequence upon a warning lightmounted on the driver-side rear-view mirror. The flashing light sequencemay be accompanied by audio beeps through the beeper 135 advising thedriver 150 to either speed up or to slow down in order to bring theother vehicle in view on the rear-view mirror. The ultrasonic sensor 145mounted upon the passenger-side rear-view mirror may operate in asimilar manner.

The camera 130 mounted upon the front windshield of the vehicle 105 maybe used in some cases to monitor an alertness state of the driver 150.The camera 130 may capture an image and/or a video clip of the face ofthe driver 150 and transmit the image and/or video clip to the driverwarning system 120. The processor 121 may evaluate the image and/orvideo clip to evaluate the alertness state of the driver. If theevaluation indicates that the driver 150 is inattentive, the driverwarning system 120 may try to alert the driver 150 by transmitting audiobeeps through the beeper 135.

Other devices of a driver alertness monitoring system can includesensors, such as a pressure sensor 165 located upon the steering wheelof the vehicle 105. Upon detecting a lack of grip on the steering wheel,the pressure sensor 165 may transmit a signal to the driver warningsystem 120. The driver warning system 120 may alert the driver 150 byemitting audio beeps through the beeper 135 and/or by providing hapticsignals through the steering wheel of the vehicle 105.

In one example method of operation in accordance with the disclosure,the driver warning system 120 may opt to obtain additional data fromother sources upon receiving input from the lane crossing detector (notshown) and the pressure sensor 165. The additional data, may, forexample, be obtained from the ultrasonic sensor 125 mounted upon thedriver-side rear-view mirror, and/or from the ultrasonic sensor 145mounted upon the passenger-side rear-view mirror. The driver warningsystem 120 may evaluate this additional data and determine that novehicle is present in adjacent lanes on both sides of the vehicle 105.Accordingly, the driver warning system 120 may prioritize the hapticwarning over the alert tone and may transmit the haptic warning first inan attempt to alert the driver 150. The driver warning system 120 maydeem the second warning (pertaining to the lane drift condition) asunnecessary if the driver 150 becomes alert, tightens his/her grip onthe steering wheel and corrects for the lane drift. If deemed necessary,the driver warning system 120 may issue the second warning at a latertime.

In another example implementation, the priority list may be generatedusing objective data obtained by crowdsourcing from a number ofvehicles. The vehicles may be communicatively interconnected with eachother, and/or to infrastructure elements (such as cloud storagedevices), using communication protocols such as, for example, avehicle-to-vehicle (V2V) protocol, an infrastructure-to-vehicle protocol(I2V) protocol, a vehicle-to-everything (V2X) protocol, a Wi-Fiprotocol, a Bluetooth® protocol, or any machine-to-machine protocol. Thedata gathered from the various vehicles can provide informationpertaining to topics such as accidents, near-accidents, and conditionspresent during an accident. In one example case, crowdsourced vehicledata may provide information that a larger number of vehicles wereinvolved in accident events under a certain type of situation (closetailgating, for example) in comparison to a number of vehicles involvedin collision events under a different type of situation (blind spotdriving, for example). In view of such information, the driver warningsystem 120 may use the priority list to prioritize a tailgating warningover a blind spot warning when the two conditions occur concurrently.

FIG. 2 illustrates a first example driving scenario that may beaddressable by the driver warning system 120 in accordance with thedisclosure. The driver 150 of the vehicle 105 is tailgating a vehicle205 that is traveling ahead of the vehicle 105 in the same lane of anexpressway. The radar sensor 110 mounted upon the front bumper of thevehicle 105 detects the vehicle 205 and transmits vehicle separationdistance information to the driver warning system 120. The processor 121of the driver warning system 120 evaluates the vehicle separationdistance information for detecting an event that warrants a warning. Theevaluation may be carried out, for example, by comparing the vehicleseparation distance information to a threshold separation distance. Theevaluation may indicate that the vehicle separation distance is lessthan the threshold separation distance and warrants a first driverwarning being issued to the driver 150 of the vehicle 105.

At the same time, the ultrasonic sensor 145 mounted upon thepassenger-side rear-view mirror of the vehicle 105 detects a vehicle 210traveling in an adjacent lane in a driver blind spot. The ultrasonicsensor 145 transmits information to the driver warning system 120 aboutthe presence of the vehicle 210 in the driver blind spot. The processor121 of the driver warning system 120 determines that a second driverwarning has to be issued to the driver 150 in addition to the firstwarning about the violation of the threshold separation distance.However, rather than providing both driver warnings concurrently, theprocessor 121 may refer to a priority list to make a determination as towhich of the two driver warnings is to be assigned a higher priority.

Upon referring to the priority list, the driver warning system 120 maydetermine that the violation of the threshold separation distancebetween the vehicle 105 and the vehicle warrants a warning, whereas thepresence of the vehicle 210 in the driver blind spot does not warrant awarning as long as the driver is not attempting to switch into the lanein which the vehicle 210 is traveling.

The driver warning may be issued, for example, in the form of a messagedisplayed on the display screen of the infotainment system 140recommending that the driver 150 increase the separation distance fromthe vehicle 205. The second driver warning pertaining to the vehicle 210traveling in the driver blind spot may be deemed unnecessary by theprocessor 121 if the driver 150 slows down the vehicle 105, therebycausing the vehicle 210 to automatically move out of the driver blindspot.

The priority list referred to with respect to the scenario illustratedin FIG. 2, can be generated using crowdsourcing techniques such as thosedescribed above (driver-based and/or vehicle-based), and may includeprioritization that is based on various threshold separation distances.For example, information obtained via crowdsourcing may provide anindication that a threshold separation distance of 10 feet or less (forexample) led to a 50% increase in accident rates in comparison toaccident rates associated with threshold separation distances thatexceeded 10 feet.

FIG. 3 illustrates a second example driving scenario that may beaddressable by the driver warning system 120 in accordance with thedisclosure. The driver 150 of the vehicle 105 is tailgating the vehicle205 that is traveling ahead of the vehicle 105 in the same lane of anexpressway. The radar sensor 110 mounted upon the front bumper of thevehicle 105 detects the vehicle 205 and transmits vehicle separationdistance information to the driver warning system 120. The processor 121of the driver warning system 120 evaluates the separation distanceinformation, for example, by comparing the vehicle separation distanceto a first threshold separation distance. The first threshold separationdistance is indicated by a dashed line 315. The dashed line 315 has anoval shape in this example implementation and can have other shapes inother implementations (rectangle, square, asymmetric, etc.). Theevaluation by the processor 121 may indicate that the vehicle separationdistance information between the vehicle 105 and the vehicle 205 is lessthan the first threshold separation distance and warrants a first driverwarning being issued to the driver 150 of the vehicle 105.

At the same time, the ultrasonic sensor 145 mounted upon thepassenger-side rear-view mirror of the vehicle 105 detects a vehicle 210traveling in an adjacent lane and moving into the lane in which thevehicle 105 is moving. The ultrasonic sensor 145 transmits informationto the driver warning system 120 about a vehicle separation distancebetween the vehicle 105 and the vehicle 210. The processor 121 of thedriver warning system 120 evaluates the separation distance information,for example, by comparing the vehicle separation distance to a secondthreshold separation distance. The second threshold separation distanceis indicated by a dashed line 320. In some implementations, the secondthreshold distance may be the same as the first threshold distance andthe dashed line 315 may be used for evaluation by the processor 121 inlieu of the dashed line 320. The processor 121 of the driver warningsystem 120 determines that a second driver warning has to be issued tothe driver 150 along with the first driver warning. However, rather thanproviding both warnings concurrently, the processor 121 may make adetermination as to which of the two warnings is to be assigned a higherpriority.

In this example scenario, the processor 121 may refer to guidance and/ora priority list stored in a database (not shown). The guidance and/orpriority list may indicate to the processor 121 that the violation ofthe first threshold separation distance between the vehicle 105 and thevehicle 205 presents an event that needs to be addressed first becauseof certain factors, such as, for example, traffic laws.

Accordingly, the driver warning system 120 transmits a driver warning,such as, in the form of a message displayed on the display screen of theinfotainment system 140 recommending that the driver 150 increase theseparation distance from the vehicle 205. Actions taken by the driver150 in reaction to the issued driver warning may be evaluated by thedriver warning system 120. The evaluation may be carried out bycommunicating with the vehicle computer 170.

The warning related to the vehicle 210 may be deemed unnecessary by theprocessor 121, if the driver 150 slows down the vehicle 105, therebyallowing the driver 211 of the vehicle 210 to either move in ahead ofthe vehicle 105 or to refrain from switching lanes.

The priority list referred to with respect to the scenario illustratedin FIG. 3 can be generated using driver-based and/or vehicle-basedcrowdsourcing techniques. The driver warning system 120 may refer tosuch a priority list and provide a driver warning that informs thedriver 150 that historical data based on crowdsourcing indicates ahigher incidence of accidents when tailgating.

FIG. 4 illustrates a third example driving scenario that may beaddressable by the driver warning system 120 in accordance with thedisclosure. In this scenario, the vehicle 210 that is traveling in anadjacent lane is moving into the lane in which the vehicle 105 ismoving. The processor 121 of the driver warning system 120 evaluatesinformation provided by the ultrasonic sensor 145 and determines that afirst driver warning has to be issued to the driver 150 about theapproaching vehicle 210.

In this example scenario, there is no vehicle in front of the vehicle105. However, the driver warning system 120 detects that there is acurve 305 in the road ahead. This detection may be carried out forexample, by evaluating a video stream provided by the front-facingcamera 115 or by utilizing map data that includes road metadata, such asroad curvature. Upon detecting the curve 305, the driver warning system120 may communicate with the vehicle computer 170 to obtain informationabout the current speed of the vehicle 105. The speed information may beevaluated by the processor 121 in conjunction with other parameters,such as, for example, road-related factors (recommended speed,curvature, length, gradient, etc.), weather factors (rainy, snowing,icy, etc.), vehicle characteristics (size, weight, and type of vehicle,etc.), and physical attributes of the driver 150. The evaluation mayindicate that the speed of the vehicle 105 meets the threshold thatwarrants a warning for negotiating the curve 305 in the road ahead.

The driver warning system 120 may, at this time, refer to a prioritylist, and/or obtain historical data, to prioritize the warning relatedto the curve 305 in the road ahead, over the warning associated with thevehicle 210 approaching the vehicle 105. Based on the priority listand/or historical data, the driver warning system 120 may transmit adriver warning (such as, in the form of a message displayed on thedisplay screen of the infotainment system 140) recommending that thedriver 150 slow down. Actions taken by the driver 150 in reaction to theissued driver warning may be evaluated by the driver warning system 120to determine if the other warning has to be issued. In one case, thedriver 211 of the vehicle 210 may notice that the vehicle 105 is slowingdown and may follow suit by slowing down and staying in his/her lane. Inthis case, the driver warning system 120 may deem the second warningunnecessary and refrain from issuing the warning.

FIG. 5 shows some example components that can be included in the driverwarning system 120 in accordance with an embodiment of the disclosure.The driver warning system 120 may include a processor 121, acommunication system 505, driver warning system hardware 510, and amemory 515. The communication system 505 can include a wirelesstransceiver that allows the driver warning system 120 to communicatewith various devices in the vehicle 105 and outside the vehicle 105. Thewireless transceiver may use any of various communication formats, suchas, for example, a vehicle-to-everything (V2X) communication format, anInternet communications format, or a cellular communications format.

The driver warning system hardware 510 can include items, such asinput/output interfaces that communicatively couple the driver warningsystem 120 to sensors and detectors in the vehicle 105. The sensors anddetectors can include, for example, the rear-facing camera 155, theradar sensor 160, the front-facing camera 115, the radar sensor 110, theultrasonic sensor 125, the ultrasonic sensor 145, and the camera 130.

The memory 515, which is one example of a non-transitorycomputer-readable medium, may be used to store an operating system (OS)540, a database 535, and code modules, such as a sensor signalprocessing module 520, a driver warning system module 525, and awarnings prioritization module 530. The code modules are provided in theform of computer-executable instructions that can be executed by theprocessor 121 for performing various operations in accordance with thedisclosure.

The sensor signal processing module 520 and the driver warning systemmodule 525 may be executed by the processor 121 for receiving signalsfrom the driver warning system hardware 510 and evaluating the signalsfor determination of events that warrant a warning. In one examplescenario, the sensor signal processing module 520 may be executed by theprocessor 121 to process radar signals received from the radar sensor110 mounted upon the front bumper of the vehicle 105 and from theultrasonic sensor 145 mounted upon the passenger-side rear-view mirrorof the vehicle 105 (illustrated in FIG. 3). The driver warning systemmodule 525 may be executed in cooperation with the sensor signalprocessing module 520 to determine the vehicle separation distancebetween the vehicle 105 and the vehicle 205 and the separation distancebetween the vehicle 105 and the vehicle 210 traveling in the adjacentlane.

The processor 121 may compare the first vehicle separation distance to afirst threshold separation distance and issues a driver warning if thefirst vehicle separation distance between the vehicle 105 and thevehicle 205 is less than the first threshold separation distance. Theprocessor 121 may also compare the second vehicle separation distance tothe second threshold separation distance and issue a driver warning ifthe second vehicle separation distance between the vehicle 105 and thevehicle 210 is less than the second threshold separation distance. Thefirst threshold separation distance and the second threshold separationdistance may be generated by the driver warning system module 525 basedon historical data stored in the database 535 and/or on personalpreferences of the driver 150.

In another scenario, the sensor signal processing module 520 and thedriver warning system module 525 may be executed by the processor 121 toevaluate images received from the front-facing camera 115 and/or therear-facing camera 155 (via the driver warning system hardware 510) inorder to evaluate events within driving scenarios. Images provided bythe front-facing camera 115 (in the form of a video stream, forexample), may be evaluated by the sensor signal processing module 520 toidentify the curve 305 in the road ahead as illustrated in FIG. 4. Thedriver warning system module 525 is executed in cooperation with thesensor signal processing module 520 to generate a driver warning in themanner described above upon identifying the curve 305. The warningsprioritization module 530 may be executed by the processor 121 todetermine priorities of various driver warnings. The database 535 mayinclude a priories list that is used by the processor 121 for thispurpose.

FIG. 6 illustrates a configuration that allows the driver warning system120 to communicate with components outside the vehicle for sharinginformation pertaining to driver warnings in accordance with anembodiment of the disclosure. In this example, the driver warning system120 is communicatively coupled via a network 605 to a server computer610 and cloud storage 615.

The network 605 may include any one, or a combination of networks, suchas a local area network (LAN), a wide area network (WAN), a telephonenetwork, a cellular network, a cable network, a wireless network, and/orprivate/public networks, such as the Internet. For example, the network605 may support communication technologies, such as TCP/IP, Bluetooth,cellular, near-field communication (NFC), Wi-Fi, Wi-Fi direct,machine-to-machine communication, and/or man-to-machine communication.

Some or all portions of a wireless communication link 601 that supportscommunications between the driver warning system 120 and a communicationdevice, such as a router, for example, that may be included in thenetwork 605, can be implemented using various types of wirelesstechnologies, such as Bluetooth®, ZigBee®, or near-field-communications(NFC), cellular, Wi-Fi, Wi-Fi direct, machine-to-machine communication,man-to-machine communication, and/or a vehicle-to-everything (V2X)communication.

Information shared between the driver warning system 120, the servercomputer 610, and/or the cloud storage 615 can be bi-directional innature. For example, in one case, driver warning information that may beuseful to multiple drivers may be transferred from the driver warningsystem 120 in the vehicle 105 to cloud storage 615. Such informationstored in cloud storage 615 may be accessed and used by driver warningsystems of various vehicles.

In another case, driver warning information that is customized to thedriver 150 may be transferred from the driver warning system 120 in thevehicle 105 to the server computer 610 and used by the driver warningsystem 120 for issuing customized warnings to the driver 150. In anothercase, the driver warning system 120 may obtain driver warninginformation stored in cloud storage 615 by other drivers or otherentities.

The driver warning information and other information, such astraffic-related statistics and warning prioritization, stored incomponents, such as the server computer 610, cloud storage 615, and thedatabase 535 of the driver warning system 120, can be generated invarious ways. In one example implementation, an entity, such as avehicle manufacturer or an original equipment manufacturer (OEM), cancollect historical data from a number of highly connected vehicles,and/or via crowdsourcing from various drivers of various vehicles. Suchinformation may be evaluated by various driver warning systems fordefining various types of driver warning signals and assigning priorityto the driver warning signals. The historical data, which can provideinformation regarding accidents, near misses, and normal drivingactions, can include information, such as a sequence of eventsassociated with an untoward traffic incident, weather conditions duringthe untoward traffic incident, accident statistics associated with asite of the untoward traffic incident, and/or information on a type ofvehicle involved in the untoward traffic incident.

In one example implementation, various driver warning signals may beprioritized by evaluating a sequence of events leading up to a prioraccident and/or a sequence of events leading up to prior avoidance of anaccident. Such evaluation may not only provide information about thesequence of events, but also provide additional information, such as ahigh incidence of certain adverse events on a particular stretch ofroad, a frequency of occurrence of adverse events, and a time of dayand/or weather conditions during occurrence of such adverse events.

A first example of a sequence of events leading to a prior accident mayinvolve a vehicle speeding up in a blind spot of a neighboring vehiclewhen traveling on a windy road segment, thereby leading to a first typeof side impact accident. A second example of a sequence of eventsleading to a prior accident may involve a vehicle speeding up in a blindspot of a neighboring vehicle on a road with poor lane markings duringpoor visibility (darkness, rain, snow, etc.), thereby leading to a sideimpact accident.

A third example of a sequence of events leading to avoidance of a prioraccident may involve a vehicle speeding up in a blind spot of aneighboring vehicle on a road with poor lane markings, on a bright sunnyday with good visibility. The drivers of the two vehicles may recognizethe speed-up maneuver and take action to avoid an accident.

The driver warning system 120 can incorporate various techniques forevaluating historical data for purposes of generating priorities andrecommendations associated with driver signal warnings. For example, thedriver warning system 120 may use a machine learning model to evaluatehistorical data. Evaluating historical data may include the use ofindependent variables such as, for example, weather, road condition, anddriver attributes, and may also include dependent variables such as, forexample, whether an event led to an accident, and the proximity ofneighboring vehicles during the occurrence of certain events (todetermine near accidents). In one example implementation, a gradientboosting machine based on decision trees or random forest can be trainedusing various types of historical data to predict an untoward trafficincident. The training can include the use of parameters, such as timeseries signals, aggregations, mathematical formulae, and statistics(average, standard deviation, maximum, minimum, rate of change, etc.).

The driver warning system 120 may not only be provided driver warningsignal priority information but may also be configured to evaluatesignals from various devices in the vehicle 105 in order to executecertain actions in accordance with the disclosure. For example, thedriver warning system 120 may be configured to evaluate signals receivedfrom the ultrasonic sensor 145 and/or the ultrasonic sensor 125 todetermine parameters, such as a threshold distance with respect to aneighboring vehicle, a separation distance between the vehicle 105 and aneighboring vehicle, an angular orientation of a neighboring vehiclewith respect to the vehicle 105. The driver warning system 120 may alsobe configured to interpret certain actions taken by drivers of othervehicles so as to provide preemptive warnings for a driver. For example,a change in speed of a neighboring vehicle and/or a lane switch of theneighboring vehicle to a different lane can be evaluated by the driverwarning system 120 to detect a potential traffic incident ahead.

In some applications, a machine learning model may be used to identifycharacteristics of various untoward traffic incidents. The driverwarning system 120 may be configured to display on the infotainmentsystem 140, messages and information that are associated with a specificuntoward event so as to alert the driver 150 to take evasive action. Twoor more messages may be displayed sequentially rather thansimultaneously, so as to allow the driver 150 to react to a prioritizedevent first before reacting to another event that occurs later. Forexample, if there is a first vehicle speeding up in a right-sideadjacent lane of the vehicle 105 and a second vehicle is slowing down ina left-side adjacent lane of the vehicle 105, driver warnings providedto the driver 150 may be based on actions carried out by the firstvehicle rather than by the second vehicle because the first vehicle maybe moving into a blind spot of the driver 150. The warnings may beprovided on the infotainment system 140 in some cases but may be issuedin other formats on other components (beeper 135, or on a display in therear-view side mirror) depending on factors, such as intensity of awarning, a priority of the warning with respect to other warnings thatmay or may not be pending, an alertness of the driver 150, a personalpreference of the driver 150, and or ambient conditions.

The driver warning system 120 may take into consideration ambientconditions when issuing certain types of warnings. For example, in ascenario where vehicle 105 is approaching another vehicle to the frontin the same lane of travel, the driver warning system 120 may issue adriver warning earlier when the roadway is wet rather than when theroadway is dry. In this example, the driver warning system 120 mayaccount for the additional time and distance needed to reduce the speedof or stop the vehicle on a wet surface.

The driver warning system 120 may also take into consideration vehiclelocation information as a parameter for evaluating road conditions. Thelocation information may be provided in various ways, such as, forexample, GPS coordinates and identifiable physical characteristics, suchas intersections, lane merges, and high curvature road edges. A learningmodel used by the processor 121 may learn local road features and roadconditions that can be used by the system to issue warnings and provideother information to the driver 150. For example, if the vehicle 105 iscoming up to a road segment with a curve in the road with a small radiusof curvature, the driver warning system 120 may provide a type ofwarning that is different than a warning related to a pending accident.The warning may include information, such as, for example, lane-relatedinformation when two through-traffic lanes become exit-only lanes. Inthis way, the driver 150 is provided more time to carefully switch to anadjacent lane while watching out for high-speed traffic in the adjacentlane. Driver warnings provided by the driver warning system 120 may alsoinclude items, such as vehicle identification about other vehicles (forexample, large semi traveling at high speed in adjacent lane).

The learning model used by the processor 121 may also be configured tolearn from historical accident data. In some cases, the learning modelcan incorporate a supervised machine learning algorithm. The supervisedlearning model algorithm may eliminate unimportant or irrelevant dataand use data that is particularly useful to the driver 150 of thevehicle 105. The supervised machine learning algorithm can be used toevaluate different types of vehicle accidents based on historicalaccident information.

In one example implementation, the supervised learning model algorithmmay use driver feedback (explicit and/or implicit feedback) to customizeand/or to refine generation and issuance of the driver warning signals.Refining driver warning signals can include eliminating issuance of sometypes of warnings, such as, for example, eliminating issuing of a driverwarning to the driver 150 regarding a separation distance from thevehicle 210 traveling in the adjacent lane when the vehicle 210 istraveling in a parallel path and is not approaching vehicle 105 (evenwhen violating the second threshold separation distance that isindicated by a dashed line 320 in FIG. 3). In some cases, a differentdriver may prefer to receive certain warnings that the driver 150 doesnot care for. Therefore, driver warning system 120 can be configured tolearn global and driver-specific preferences. An example learningprocedure may involve the use of crowdsourced data to generate a globalmodel. The global model may be used, for example, to issue warnings thatare tailored to individual drivers, based on their personal preferences.

The driver warning system 120 may also be configured to accept feedbackfrom multiple drivers, such as, for example, to identify which driverwarnings are more useful than others and/or which driver warnings areunnecessary or have nuisance value. In some cases, the driver warningsystem 120 may use an unsupervised machine learning algorithm togenerate various driver groups based on individual drivingcharacteristics. The driver 150 may be prompted to provide individualpreferences and the vehicle can learn an individual profile with regardto what information is the most relevant to be displayed to the driver150.

In the above disclosure, reference has been made to the accompanyingdrawings, which form a part hereof, which illustrate specificimplementations in which the present disclosure may be practiced. It isunderstood that other implementations may be utilized, and structuralchanges may be made without departing from the scope of the presentdisclosure. References in the specification to “one embodiment,” “anembodiment,” “an example embodiment,” “an example embodiment,” etc.,indicate that the embodiment described may include a particular feature,structure, or characteristic, but every embodiment may not necessarilyinclude the particular feature, structure, or characteristic. Moreover,such phrases are not necessarily referring to the same embodiment.Further, when a particular feature, structure, or characteristic isdescribed in connection with an embodiment, one skilled in the art willrecognize such feature, structure, or characteristic in connection withother embodiments whether or not explicitly described.

Implementations of the systems, apparatuses, devices, and methodsdisclosed herein may comprise or utilize one or more devices thatinclude hardware, such as, for example, one or more processors andsystem memory, as discussed herein. An implementation of the devices,systems, and methods disclosed herein may communicate over a computernetwork. A “network” is defined as one or more data links that enablethe transport of electronic data between computer systems and/or modulesand/or other electronic devices. When information is transferred orprovided over a network or another communications connection (eitherhardwired, wireless, or any combination of hardwired or wireless) to acomputer, the computer properly views the connection as a transmissionmedium. Transmission media can include a network and/or data links,which can be used to carry desired program code means in the form ofcomputer-executable instructions or data structures and which can beaccessed by a general purpose or special purpose computer. Combinationsof the above should also be included within the scope of non-transitorycomputer-readable media.

Computer-executable instructions comprise, for example, instructions anddata which, when executed at a processor, cause the processor to performa certain function or group of functions. The computer-executableinstructions may be, for example, binaries, intermediate formatinstructions, such as assembly language, or even source code. Althoughthe subject matter has been described in language specific to structuralfeatures and/or methodological acts, it is to be understood that thesubject matter defined in the appended claims is not necessarily limitedto the described features or acts described above. Rather, the describedfeatures and acts are disclosed as example forms of implementing theclaims.

A memory device, such as the memory 515, can include any one memoryelement or a combination of volatile memory elements (e.g., randomaccess memory (RAM, such as DRAM, SRAM, SDRAM, etc.)) and non-volatilememory elements (e.g., ROM, hard drive, tape, CDROM, etc.). Moreover,the memory device may incorporate electronic, magnetic, optical, and/orother types of storage media. In the context of this document, a“non-transitory computer-readable medium” can be, for example but notlimited to, an electronic, magnetic, optical, electromagnetic, infrared,or semiconductor system, apparatus, or device. More specific examples (anon-exhaustive list) of the computer-readable medium would include thefollowing: a portable computer diskette (magnetic), a random-accessmemory (RAM) (electronic), a read-only memory (ROM) (electronic), anerasable programmable read-only memory (EPROM, EEPROM, or Flash memory)(electronic), and a portable compact disc read-only memory (CD ROM)(optical). Note that the computer-readable medium could even be paper oranother suitable medium upon which the program is printed, since theprogram can be electronically captured, for instance, via opticalscanning of the paper or other medium, then compiled, interpreted orotherwise processed in a suitable manner if necessary, and then storedin a computer memory.

Those skilled in the art will appreciate that the present disclosure maybe practiced in network computing environments with many types ofcomputer system configurations, including in-dash vehicle computers,personal computers, desktop computers, laptop computers, messageprocessors, handheld devices, multi-processor systems,microprocessor-based or programmable consumer electronics, network PCs,minicomputers, mainframe computers, mobile telephones, PDAs, tablets,pagers, routers, switches, various storage devices, and the like. Thedisclosure may also be practiced in distributed system environmentswhere local and remote computer systems, which are linked (either byhardwired data links, wireless data links, or by any combination ofhardwired and wireless data links) through a network, both performtasks. In a distributed system environment, program modules may belocated in both the local and remote memory storage devices.

Further, where appropriate, the functions described herein can beperformed in one or more of hardware, software, firmware, digitalcomponents, or analog components. For example, one or more applicationspecific integrated circuits (ASICs) can be programmed to carry out oneor more of the systems and procedures described herein. Certain termsare used throughout the description, and claims refer to particularsystem components. As one skilled in the art will appreciate, componentsmay be referred to by different names. This document does not intend todistinguish between components that differ in name, but not function.

It should be noted that the sensor embodiments discussed above maycomprise computer hardware, software, firmware, or any combinationthereof to perform at least a portion of their functions. For example, asensor may include computer code configured to be executed in one ormore processors and may include hardware logic/electrical circuitrycontrolled by the computer code. These example devices are providedherein for purposes of illustration and are not intended to be limiting.Embodiments of the present disclosure may be implemented in furthertypes of devices, as would be known to persons skilled in the relevantart(s).

At least some embodiments of the present disclosure have been directedto computer program products comprising such logic (e.g., in the form ofsoftware) stored on any computer-usable medium. Such software, whenexecuted in one or more data processing devices, causes a device tooperate as described herein.

While various embodiments of the present disclosure have been describedabove, it should be understood that they have been presented by way ofexample only, and not limitation. It will be apparent to persons skilledin the relevant art that various changes in form and detail can be madetherein without departing from the spirit and scope of the presentdisclosure. Thus, the breadth and scope of the present disclosure shouldnot be limited by any of the above-described example embodiments butshould be defined only in accordance with the following claims and theirequivalents. The foregoing description has been presented for thepurposes of illustration and description. It is not intended to beexhaustive or to limit the present disclosure to the precise formdisclosed. Many modifications and variations are possible in light ofthe above teaching. Further, it should be noted that any or all of theaforementioned alternate implementations may be used in any combinationdesired to form additional hybrid implementations of the presentdisclosure. For example, any of the functionality described with respectto a particular device or component may be performed by another deviceor component. Further, while specific device characteristics have beendescribed, embodiments of the disclosure may relate to numerous otherdevice characteristics. Further, although embodiments have beendescribed in language specific to structural features and/ormethodological acts, it is to be understood that the disclosure is notnecessarily limited to the specific features or acts described. Rather,the specific features and acts are disclosed as illustrative forms ofimplementing the embodiments. Conditional language, such as, amongothers, “can,” “could,” “might,” or “may,” unless specifically statedotherwise, or otherwise understood within the context as used, isgenerally intended to convey that certain embodiments could include,while other embodiments may not include, certain features, elements,and/or steps. Thus, such conditional language is not generally intendedto imply that features, elements, and/or steps are in any way requiredfor one or more embodiments.

1. A method comprising: detecting, by a first sensor in a first vehicle,a first driving event; detecting, by the first sensor or a second sensorin the first vehicle, a second driving event; determining a first driverwarning associated with the first driving event and a second driverwarning associated with the second driving event; generating, based onprioritizing the first driving event or the second driving event and ata first time, the first driver warning in the first vehicle instead ofthe second driver warning, wherein generating the first driver warningfurther comprises generating the first driver warning at a firstlocation within the first vehicle for a driver; and generating, by aprocessor and at a second time, the first driver warning at a secondlocation within the vehicle.
 2. The method of claim 1, wherein the firstdriving event comprises a road condition or a weather condition, whereinthe second driving event comprises a second vehicle violating athreshold separation distance between the first vehicle and the secondvehicle, and wherein the second driving event is assigned a higherpriority than the first driving event.
 3. The method of claim 1, whereinthe second driving event is assigned a higher priority than the firstdriving event based on historical data indicating that a likelihood thatthe first driving event will result in an untoward traffic incident ishigher than a likelihood that the second driving event will result in anuntoward traffic incident.
 4. (canceled)
 5. The method of claim 1,wherein the first driver warning includes a first type of driver warningor a second type of driver warning, and wherein producing the firstdriver warning further comprises producing the first type of driverwarning instead of the second type of driver warning based on anindicated preference of a driver of the first vehicle.
 6. The method ofclaim 1, wherein prioritizing the first driving event or the seconddriving event is based on historical data, wherein the historical datais derived from weather conditions in a locality, a number of accidentsin the locality, information on a type of vehicle involved in accidentsin the locality, and/or driving characteristics of drivers in thelocality.
 7. (canceled)
 8. A method comprising: detecting, by a firstsensor in a first vehicle, a first driving event associated with a firsttype of driver warning; detecting, by a second sensor in the firstvehicle, a second driving event associated with a second type of driverwarning; receiving, from the second sensor, an indication of a conditionassociated with the second driving event; generating, by a processor andat a first time, the first type of driver warning instead of the secondtype of driver warning based on receiving the indication of thecondition associated with the second driving event, wherein generatingthe first type of driver warning further comprises generating the driverwarning at a first location within the first vehicle; and generating, bya processor and at a second time, the first type of driver warning at asecond location within the first vehicle.
 9. The method of claim 8,further comprising: detecting, by the first sensor or a second sensor inthe first vehicle, a second driving event; and generating the driverwarning based on prioritizing the first driving event or the seconddriving event.
 10. The method of claim 9, wherein the first drivingevent comprises a road condition or a weather condition, wherein thesecond driving event comprises a second vehicle violating a thresholdseparation distance between the first vehicle and the second vehicle,and wherein the second driving event is assigned a higher priority thanthe first driving event.
 11. The method of claim 10, wherein generatingthe first type of driver warning is further based on historical data,wherein the historical data comprises a sequence of events associatedwith an untoward traffic incident, weather conditions during theuntoward traffic incident, accident statistics associated with a site ofthe untoward traffic incident, and/or information on a type of vehicleinvolved in the untoward traffic incident.
 12. The method of claim 11,wherein the second driving event is assigned the higher priority basedon the sequence of events associated with the untoward traffic incidentproviding information that a likelihood that the first driving eventwill result in an untoward traffic incident is higher than a likelihoodthat the second driving event will result in an untoward trafficincident.
 13. The method of claim 9, further comprising: prioritizingthe first driving event or the second driving event based on evaluatinghistorical data.
 14. The method of claim 8, wherein generating the firsttype of driver warning at the first location is based on at least oneof: an intensity of a warning, a priority of the first type of driverwarning, an alertness of the driver, a personal preference of thedriver, or an ambient condition.
 15. A system in a first vehicle, thesystem comprising: a memory that stores computer-executableinstructions; and a processor configured to access the memory andexecute the computer-executable instructions to: receive an indicationof a first driving event associated with a first type of driver warningand a second driving event associated with a second type of driverwarning; determine that the first driving event is associated with ahigher priority than the second driving event; and generate a driverwarning for the first driving event at a first time based on the thedetermination that the first driving event is associated with the higherpriority than the second driving event, wherein the first type of driverwarning is generated at a first location within the first vehicle; andgenerate a driver warning for the first driving event at a second timeat a second location within the first vehicle.
 16. The system of claim15, further comprises a second sensor configured to detect a seconddriving event, and wherein the processor is configured to access thememory and execute computer-executable instructions to: generate thedriver warning based on prioritizing the first driving event or thesecond driving event.
 17. The system of claim 16, wherein the firstdriving event comprises a road condition or a weather condition, whereinthe second driving event comprises a second vehicle violating athreshold separation distance between the first vehicle and the secondvehicle, and wherein the second driving event is assigned a higherpriority than the first driving event.
 18. The system of claim 17,wherein the processor is configured to access the memory and executecomputer-executable instructions to: receive historical data based inpart on the first driving event and the second driving event, whereinthe historical data comprises a sequence of events associated with anuntoward traffic incident, weather conditions during the untowardtraffic incident, accident statistics associated with a site of theuntoward traffic incident, and/or information on a type of vehicleinvolved in the untoward traffic incident.
 19. The system of claim 18,wherein the processor is configured to access the memory and executecomputer-executable instructions to: determine, based on historicaldata, that the second driving event precedes an accident.
 20. The systemof claim 18, wherein the historical data is derived from a driver of thefirst vehicle, multiple drivers of the first vehicle, and/or multipledrivers of multiple vehicles.
 21. (canceled)
 22. The method of claim 8further comprising; determining an action taken by a driver of the firstvehicle subsequent to generating the first type of driver warning; anddetermining, based on the action, an absence of the first driving eventand the second driving event.
 23. The method of claim 1, furthercomprising: receiving feedback from the driver; training, based on thefeedback, a supervised learning model; and generating, at a third time,and based on the supervised learning model, the first driver warningwithin the first vehicle.